Data science is quickly becoming one of the most promising careers in the twenty-first century. It is automated, program-driven, and analytical. As a result, it’s no surprise that the demand for data scientists has been expanding in the job market over the last few years.
We will begin with a quick refresher on Python fundamentals for beginners in this course. This is optional; if you’re already familiar with Python, skip to the next chapter.
Data science will be the topic of the next three sections. We will start with the essential Python libraries for data science, then go on to the fundamental NumPy properties, and lastly begin with mathematics and how to use it in data science.
You will learn about Python Pandas DataFrames and series after learning about data science. Following that, we will get down to business and begin data cleaning. Following that, we will learn how to use Python to visualize data and do data analysis on some sample datasets. Finally, we will cover the Time series in Python and learn how to work with and convert datasets to Time series.
By the end of this course, you will be able to execute data manipulation for data science in Python with ease.
What You Will Learn
- A quick refresher to Python fundamentals
- Learn to use Pandas for data analysis
- Learn to work with numerical data in Python
- Learn statistics and math with Python
- Learn how to code in Jupyter Notebook
- Learn how to install packages in Python
This course is open to students of all skill levels, and you will be able to succeed even if you have no prior programming or statistical knowledge.
About The Author
Meta Brains: Meta Brains is a team of passionate software developers and finance professionals. They provide professional training programs that combine their expertise in coding, finance, and Excel.
With a focus on the Metaverse, they aim to equip learners with the necessary skills to participate in the next computing revolution. Their inclusive approach ensures accessibility to everyone, fostering a community that collaboratively codes and builds the future of the Metaverse.
Table of contents
Chapter 1 : Python Quick Refresher (Optional)
- Welcome to the course!
- Introduction to Python
- Setting up Python
- What is Jupyter?
- Anaconda Installation: Windows, Mac, and Ubuntu
- How to Implement Python in Jupyter?
- Managing Directories in Jupyter Notebook
- Working with Different Datatypes
- Arithmetic Operators
- Comparison Operators
- Logical Operators
- Conditional Statements
- Sequences: Lists
- Sequences: Dictionaries
- Sequences: Tuples
- Functions: Built-in Functions
- Functions: User-Defined Functions
- Chapter 2 : Essential Python Libraries for Data Science
- Chapter 3 : Fundamental NumPy Properties
Chapter 4 : Mathematics for Data Science
- Basic NumPy Arrays: zeros()
- Basic NumPy Arrays: ones()
- Basic NumPy Arrays: full()
- Adding a Scalar
- Subtracting a Scalar
- Multiplying by a Scalar
- Dividing by a Scalar
- Raise to a Power
- Element-Wise Addition
- Element-Wise Subtraction
- Element-Wise Multiplication
- Element-Wise Division
- Matrix Multiplication
Chapter 5 : Python Pandas DataFrames and Series
- What is a Python Pandas DataFrame?
- What is a Python Pandas Series?
- DataFrame versus Series
- Creating a DataFrame Using Lists
- Creating a DataFrame Using a Dictionary
- Loading CSV Data into Python
- Changing the Index Column
- Examining the DataFrame: Head and Tail
- Statistical Summary of the DataFrame
- Slicing Rows Using Bracket Operators
- Indexing Columns Using Bracket Operators
- Boolean List
- Filtering Rows
- Filtering rows using ‘’ and ‘|’ Operators
- Filtering Data Using loc()
- Filtering Data Using iloc()
- Adding and Deleting Rows and Columns
- Sorting Values
- Exporting and Saving Pandas DataFrames
- Concatenating DataFrames
- Chapter 6 : Data Cleaning
- Chapter 7 : Data Visualization using Python
Chapter 8 : Exploratory Data Analysis
- What is Exploratory Data Analysis?
- Univariate Analysis
- Univariate Analysis: Continuous Data
- Univariate Analysis: Categorical Data
- Bivariate Analysis: Continuous and Continuous
- Bivariate Analysis: Categorical and Categorical
- Bivariate Analysis: Continuous and Categorical
- Detecting Outliers
- Categorical Variable Transformation
- Chapter 9 : Time Series in Python
- Title: Data Manipulation in Python - Master Python, NumPy, and Pandas
- Release date: May 2022
- Publisher(s): Packt Publishing
- ISBN: 9781804614396
You might also like
Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python
Welcome to the course where you will learn about the NumPy stack in Python, which is …
Programming with Data: Python and Pandas LiveLessons
5 Hours of Video Instruction Learn how to use Pandas and Python to load and transform …
Pandas Data Analysis with Python Fundamentals
3+ Hours of Video Instruction provides analysts and aspiring data scientists with a practical introduction to …
Pandas Data Cleaning and Modeling with Python
The perfect follow up to Pandas Data Analysis with Python Fundamentals LiveLessons for the aspiring data …